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基于原位模拟的胸腔镜机器人手术急症临床系统测试的应用。

Use of In-Situ Simulation Based Clinical Systems Test of Thoracic Robotic Surgery Emergencies.

机构信息

Division of Thoracic Surgery, UMASS T.H. Chan School of Medicine, Worcester, Massachusetts.

Department of Anesthesia, UMASS T.H. Chan School of Medicine, Worcester, Massachusetts.

出版信息

J Surg Res. 2022 Aug;276:37-47. doi: 10.1016/j.jss.2022.02.042. Epub 2022 Mar 22.

Abstract

INTRODUCTION

With the advancement of robotic surgery, some thoracic surgeons have been slow to adopt to this new operative approach, in part because they are un-scrubbed and away from the patient while operating. Aiming to allay surgeon concerns of intra-operative emergencies, an insitu simulation-based clinical system's test (SbCST) can be completed to test the current clinical system, and to practice low-frequency, high-stakes clinical scenarios with the entire operating room (OR) team.

METHODS

Six different OR teams completed an insitu SbCST of an intra-operative pulmonary artery injury during a robot-assisted thoracic surgery at a single tertiary care center. The OR team consisted of an attending thoracic surgeon, surgery resident, anesthesia attending, anesthesia resident, circulating nurse, and a scrub technician. This test was conducted with an entire OR team along with study observers and simulation center staff. Outcomes included the identified latent safety threats (LSTs) and possible solutions for each LST, culminating in a complete failure mode and effects analysis (FMEA). A Risk Priority Number (RPN) was determined for each LST identified. Pre- and post-simulation surveys using Likert scales were also collected.

RESULTS

The six FMEAs identified 28 potential LSTs in four categories. Of these 28 LSTs, nine were considered high priority based on their Risk Priority Number (RPN) with seven of the nine being repeated multiple times. Pre- and post-simulation survey responses were similar, with the majority of participants (94%) agreeing that high fidelity simulation of intra-operative emergencies is helpful and provides an opportunity to train for high-stakes, low-frequency events. After completing the SbCST, more participants felt confident that they knew their role during an intra-operative emergency than their pre-simulation survey responses. All participants agreed that simulation is an important part of continuing education and is helpful for learning skills that are infrequently used. Following the SbCST, more participants agreed that they knew how to safely undock the da Vinci robot during an emergency.

CONCLUSIONS

SbCSTs provide an opportunity to test the current clinical system with a low-frequency, high-stakes event and allow medical personnels to practice their skills and teamwork. By completing multiple SbCSTs, we were able to identify multiple LSTs within different OR teams, allowing for a broader review of the current clinical systems in place. The use of these SbCSTs in conjunction with debriefing sessions and FMEA completion allows for the most significant potential improvement of the current system. This study shows that SbCST with FMEA completion can be used to test current systems and create better systems for patient safety.

摘要

简介

随着机器人手术的进步,一些胸外科医生对这种新的手术方法反应迟缓,部分原因是他们在手术过程中不戴手套,远离患者。为了消除外科医生对术中紧急情况的担忧,可以进行原位模拟临床系统测试(SbCST),以测试当前的临床系统,并与整个手术室(OR)团队一起练习低频、高风险的临床场景。

方法

六支不同的 OR 团队在一家三级护理中心完成了一次机器人辅助胸部手术中术中肺动脉损伤的原位 SbCST。OR 团队由一名主治胸外科医生、外科住院医师、麻醉主治医生、麻醉住院医师、巡回护士和一名刷手技术员组成。这项测试是由整个 OR 团队、研究观察员和模拟中心工作人员共同进行的。结果包括确定每个 LST 的潜在安全威胁(LST)和可能的解决方案,最终完成完整的失效模式和影响分析(FMEA)。为每个确定的 LST 确定了一个风险优先数(RPN)。还收集了模拟前后使用李克特量表的调查。

结果

六次 FMEA 确定了四个类别中的 28 个潜在 LST。在这 28 个 LST 中,有 9 个基于其风险优先数(RPN)被认为是高优先级的,其中 7 个被多次重复。模拟前后的调查结果相似,大多数参与者(94%)都同意高保真模拟术中紧急情况是有帮助的,并为高风险、低频率事件提供了培训机会。完成 SbCST 后,更多的参与者感到有信心在术中紧急情况下知道自己的角色,而不是他们模拟前的调查反应。所有参与者都同意模拟是继续教育的重要组成部分,有助于学习很少使用的技能。在完成 SbCST 后,更多的参与者同意他们知道如何在紧急情况下安全地卸下达芬奇机器人。

结论

SbCST 提供了一个机会,可以用低频、高风险的事件来测试当前的临床系统,并让医务人员练习他们的技能和团队合作。通过完成多次 SbCST,我们能够在不同的 OR 团队中识别出多个 LST,从而更广泛地审查当前的临床系统。使用这些 SbCST 结合讨论和 FMEA 完成,可以对当前系统进行最大程度的改进。这项研究表明,使用 SbCST 完成 FMEA 可以用于测试当前系统并创建更安全的系统。

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